Automated equalization

ABSTRACT

Techniques for improving speech recognition are described. An example of an electronic device includes an extracting unit to extract a reference spectral profile from a reference signal and a device spectral profile from a device signal. A comparing unit compares the reference spectral profile and the device spectral profile. A delta calculating unit calculates a delta between the reference spectral profile and the device spectral profile. A design unit designs a correction filter based on the computed delta.

TECHNICAL FIELD

The present disclosure relates generally to techniques for equalizationof a microphone internal to a device to improve the accuracy of speechrecognition by the device. More specifically, the present techniquesrelate to the calculation of a correction filter which is applied to asignal produced by the microphone.

BACKGROUND ART

Automatic speech recognition (ASR) systems enable the translation ofspoken language into text by a computer or computerized device. Theperformance of ASR systems varies from one device to another partlybecause of the differences in the frequency response of the devices'microphones.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an electronic device for improvingautomatic speech recognition;

FIG. 2 is a process flow diagram of a method for improving automaticspeech recognition;

FIG. 3 is a block diagram showing a medium that contains logic forimproving automatic speech recognition;

FIG. 4A is an example of a reference spectral profile obtained using thepresent techniques;

FIG. 4B is an example of a spectral profile for a device under test(DUT) obtained using the present techniques;

FIG. 4C is an example of a DUT spectral profile after application of acorrection filter obtained using the present techniques;

FIG. 4D is an example of a magnitude response of a correction filterobtained using the present techniques;

FIG. 4E is an example of accuracy improvements in automatic speechrecognition (ASR) resulting from application of a correction filterobtained using the present techniques; and

FIG. 4F is an example of accuracy improvements in ASR resulting fromapplication of a correction filter obtained using the presenttechniques.

The same numbers are used throughout the disclosure and the figures toreference like components and features. Numbers in the 100 series referto features originally found in FIG. 1; numbers in the 200 series referto features originally found in FIG. 2; and so on.

DESCRIPTION OF THE EMBODIMENTS

The subject matter disclosed herein relates to techniques for improvingspeech recognition of a computerized device. The present disclosuredescribes techniques for improving speech recognition by calculating andapplying a correction filter. For example, a reference signal may becalculated from a set of speech recordings. A device signal may beobtained by recording a speech utterance. A reference spectral profilemay be extracted from the reference signal and a device spectral profilemay be extracted from the device signal. The reference spectral profileand the device spectral profile may be compared. A delta between thereference spectral profile and the device spectral profile may becalculated. A correction filter may be designed using the computeddelta. The correction filter may be applied to a signal produced by thedevice's microphone. Correction filters may be designed for differentcircumstances and for various microphones in multi-microphone devices.Various examples of the present techniques are described further belowwith reference to the figures.

In the following description and claims, the terms “coupled” and“connected,” along with their derivatives, may be used. It should beunderstood that these terms are not intended as synonyms for each other.Rather, in particular embodiments, “connected” may be used to indicatethat two or more elements are in direct physical or electrical contactwith each other. “Coupled” may mean that two or more elements are indirect physical or electrical contact. However, “coupled” may also meanthat two or more elements are not in direct contact with each other, butyet still co-operate or interact with each other.

FIG. 1 is a block diagram of an electronic device 100 for improvingautomatic speech recognition. For example, the electronic device 100 maybe a desktop computer, laptop computer, tablet computer, mobile phone,smart phone, or any other suitable electronic device. The electronicdevice 100 may include a central processing unit (CPU) 102 that isconfigured to execute stored instructions, as well as a memory device104 that stores instructions that are executable by the CPU 102. The CPU102 may be coupled to the memory device 104 by a bus 106. The CPU 102may be a single core processor, a multi-core processor, a computingcluster, or any number of other configurations. The CPU 102 may beimplemented as a Complex Instruction Set Computer (CISC) processor, aReduced Instruction Set Computer (RISC) processor, x86 Instruction setcompatible processor, or any other microprocessor or central processingunit. In some embodiments, the CPU 102 includes dual-core processor(s),dual-core mobile processor(s), or the like.

The memory device 104 may include random access memory (e.g., SRAM,DRAM, zero capacitor RAM, SONOS, eDRAM, EDO RAM, DDR RAM, RRAM, PRAM,etc.), read only memory (e.g., Mask ROM, PROM, EPROM, EEPROM, etc.),flash memory, or any other suitable memory system. The memory device 104can be used to store data and computer-readable instructions that, whenexecuted by the CPU 102, direct the CPU 102 to perform variousoperations in accordance with embodiments described herein.

The electronic device 100 may also include a storage drive 106. Thestorage device 106 is a physical memory device such as a hard drive, anoptical drive, a flash drive, an array of drives, or any combinationsthereof. The storage device 106 may store data such as audio signals andspectral profiles, among other types of data. The storage device 106 mayalso store programming code such as device drivers, softwareapplications, operating systems, and the like. The programming codestored by the storage device 106 may be executed by the CPU 102 or anyother processors that may be included in the electronic device 100.

The electronic device 100 may also include an input/output (I/O) deviceinterface 108 configured to connect the electronic device 100 to one ormore I/O devices 110. For example, the I/O devices 110 may include aprinter, a scanner, a keyboard, and a pointing device such as a mouse,touchpad, or touchscreen, among others. The I/O devices 110 may bebuilt-in components of the electronic device 100, or may be devices thatare externally connected to the electronic device 100.

The electronic device 100 may also include a network interfacecontroller (NIC) 112 configured to connect the electronic device 100 toa network 114. The network 114 may be a wide area network (WAN), localarea network (LAN), or the Internet, among others.

The electronic device 100 may be a stand-alone device or a component ofa device under test (DUT). The DUT is the device to which the correctionfilter will be applied.

The electronic device 100 may include a first calculating unit (notshown) to calculate a reference signal from a set of recordings. Toobtain a DUT signal, a microphone internal to the DUT may receive aspeech utterance. The speech utterance received by the microphone may beconverted into an electrical signal containing information thatrepresents the speech utterance. This signal may be the DUT signal. DUTsignals may be obtained for different microphones of the DUT, fordifferent orientations of the DUT (e.g., portrait or landscape),different distances from a user to the DUT, and different angles betweena user and the DUT. The different DUT signals may be used to constructdifferent correction filters that are applied depending on the DUTmicrophone used, the DUT's orientation, and a user's position relativeto the DUT. A recording unit (not shown) may record the various DUTsignals. In some embodiments, the DUT itself may record the speechutterances.

The electronic device 100 may also include an extracting unit 116 toextract a reference spectral profile from the reference signal and a DUTspectral profile from the DUT signal. A comparing unit 118 may comparethe reference spectral profile and the DUT spectral profile. A deltacalculating unit 120 may calculate a delta between the two spectralprofiles. The computed delta may serve as a basis for the design of thecorrection filter by the design unit 122. An application unit (notshown) may apply the correction filter to the signal emanating from theDUT's microphone.

Different correction filters may be applied depending on the DUTmicrophone used, the DUT's orientation, and the user's position relativeto the DUT. An orientation sensor (not shown) may determine theorientation of the DUT and apply the appropriate correction filter. Forexample, if a user is speaking and the DUT is in portrait mode, theapplied correction filter may be the filter constructed from the DUTsignal received while the DUT had a portrait orientation. In anotherexample, different correction filters may be applied depending onwhether the lid of the DUT (e.g., a laptop) was open or closed when theuser was speaking.

The electronic device 100 may also include a proximity sensor (notshown) to determine the distance from a speaker to the DUT and apply theappropriate correction filter. For example, if the speaker is two feetfrom the DUT, the applied correction filter may be the filterconstructed from the DUT signal received from two feet away.

The electronic device 100 may include an angle sensor (not shown) todetermine the angle between a speaker and the DUT and apply theappropriate correction filter. For example, if the speaker is at anangle of 30° to the DUT, the applied correction filter may be the filterconstructed from the DUT signal received at an angle of 30°. In thisway, the orientation sensor, proximity sensor, and angle sensor maydetermine the correction filter that is applied to the signalpropagating from the DUT's microphone.

Communication between various components of the electronic device 100may be accomplished via one or more busses 106. At least one of thebusses 106 may be a D-PHY bus, a Mobile Industry Processor Interface(MIPI) D-PHY bus, or an M-PHY bus, or any other suitable bus.

The bus architecture shown in FIG. 1 is just one example of a busarchitecture that may be used with the techniques disclosed herein. Insome examples, the bus 106 may be a single bus that couples all of thecomponents of the electronic device 100 according to a particularcommunication protocol. Furthermore, the electronic device 100 may alsoinclude any suitable number of busses 106 of varying types, which mayuse different communication protocols to couple specific components ofthe electronic device according to the design considerations of aparticular implementation.

The block diagram of FIG. 1 is not intended to indicate that thecomputing device 100 is to include all of the components shown inFIG. 1. Rather, the computing system 100 can include fewer or additionalcomponents not shown in FIG. 1, depending on the details of the specificimplementation. Furthermore, any of the functionalities of the CPU 102may be partially, or entirely, implemented in hardware and/or by aprocessor. For example, the functionality may be implemented in anycombination of Application Specific Integrated Circuits (ASICs), FieldProgrammable Gate Arrays (FPGAs), logic circuits, and the like. Inaddition, embodiments of the present techniques can be implemented inany suitable electronic device, including ultra-compact form factordevices, such as System-On-a-Chip (SOC), and multi-chip modules.

FIG. 2 is a process flow diagram of a method 200 for improving automaticspeech recognition. The method 200 may be performed by the electronicdevice 100 shown in FIG. 1. The method 200 may begin by obtaining areference signal and a DUT signal (not shown). The reference signal maybe computed using a set of speech recordings. The speech recordings mayhave a high accuracy, i.e., low word error rate, when used with aparticular automatic speech recognition (ASR) system. Different speechrecordings may be obtained using different ASR systems. The differentspeech recordings may be used to construct correction filters applicableto specific ASR systems. For example, if a speech recording is obtainedusing a particular ASR system, the correction filter may be used withthat particular system. Suitable ASR systems are available from a numberof manufacturers and/or organizations.

The DUT signal may be constructed in a series of steps. A speechutterance may be recorded using the DUT to obtain a recorded speechutterance. The speech utterance may be recorded under differentcircumstances, i.e., different orientations of the DUT and differentpositions of the speaker relative to the DUT. The circumstances underwhich the recording was made may determine the circumstances under whichthe correction filter will be applicable. For example, if the recordingwas obtained when the DUT was in landscape mode and the speaker was 3feet away from the DUT at an angle of 45° to the DUT, the resultingcorrection filter may be applied under the same circumstances. In otherexamples, the correction filter may have an average spectral profileobtained from recordings made while one condition is held constant(e.g., portrait or landscape mode) and the other conditions are varied.

The recorded speech utterance may be broadcast using a speaker externalto the DUT to obtain a broadcasted speech utterance. The broadcastedspeech utterance may be received via a microphone or microphonesinternal to the DUT. Once received by the microphone, the broadcastedspeech utterance may become a DUT signal.

The method 200 may include blocks 202-212. At block 202, a referencespectral profile may be extracted from the reference signal by theextracting unit 116. The amplitude spectra of the reference signal maybe time averaged to yield an averaged reference amplitude spectrum. Theaveraged reference amplitude spectrum constitutes the reference spectralprofile. For example, the reference spectral profile may be computed asan average of short time amplitude spectra of the reference signal.Short time magnitude spectra may also be averaged. The average may becalculated across adjacent time frames of the reference signal. Eachtime frame may be converted to the frequency domain by the fast Fouriertransform (FFT), the cosine transform, and the like. The resolution ofthe spectral profile may be selected to match the Mel-filteringresolution defined in the ETSI ES 201 108 standard, “Speech Processing,Transmission and Quality Aspects.” The standard describes a distributedspeech recognition system that overcomes the degradation of theperformance of speech recognition systems receiving speech transmittedover mobile channels. Matching of the spectral resolution to theMel-filtering resolution may be performed because Mel filters aretypically used to analyze signals in ASR systems. In practice, thematching of the resolutions may mean a 512-point FFT is utilized.

At block 204, a DUT spectral profile may be extracted from the DUTsignal by the extracting unit 116. The DUT spectral profile may becalculated in the same manner as the reference spectral profile. Atblock 206, the reference spectral profile and the DUT spectral profilemay be compared by the comparing unit 118.

At block 208, the delta between the reference spectral profile and theDUT spectral profile may be calculated by the delta calculating unit120. The delta may be defined as:D(f)=P _(REF)(f)/P _(DUT)(f)where

-   f—frequency-   P_(REF)(f)—reference spectral profile on linear scale-   P_(DUT)(f)—DUT spectral profile on linear scale

At block 210, D(f) may be used as a reference to design the correctionfilter. This may be accomplished by the design unit 122. For example,the correction filter may be designed using a frequency-sampling designmethod and implemented as a finite impulse response filter or thecorrection filter may be designed by any other suitable method. It maybe possible to construct different correction filters for the differentsituations simulated during recording of the speech utterance by theDUT. For example, one recording may have occurred with the DUT inlandscape mode and the speaker 3 feet away from the DUT at an angle of45° to the DUT. In that case, P_(DUT)(f) may be extracted from therecordings made for that particular set of circumstances. Hence, D(f)and the resulting correction filter may be specific to that situation.

At block 212, the correction filter may be applied to the signalproduced by the microphone internal to the DUT. For example, thecorrection filter may be applied to the microphone's signal in real timeby adding a filtering block to the pre-processing (up-link) pipeline.Pipelines of this type are often used for audio stacks in notebookcomputers, tablet computers, mobile phones, smart phones, and the like.

Application of the correction filter to the DUT's microphone signal mayinclude determining the orientation of the DUT, the distance of a userfrom the DUT, the angle between the user and the DUT, and the positionof the DUT's lid. These determinations may be made to ascertain whichcorrection filter is appropriate given the set of circumstances. In thismanner, the correction filter that is applied may be derived from therecordings made under the same or similar circumstances.

Blocks 202-210 may be referred to as the tuning stage and may beperformed by the manufacturer of the DUT. Block 212 may be performedwhen a user speaks into the DUT's microphone.

FIG. 3 is a block diagram showing a medium 300 that contains logic forobtaining a correction filter. The medium 300 may be a non-transitorycomputer-readable medium that stores code that can be accessed by acomputer processing unit (CPU) 302 via a bus 304. For example, thecomputer-readable medium 300 can be a volatile or non-volatile datastorage device. The medium 300 can also be a logic unit, such as anApplication Specific Integrated Circuit (ASIC), a Field ProgrammableGate Array (FPGA), or an arrangement of logic gates implemented in oneor more integrated circuits, for example.

The medium 300 may include modules 306-312 configured to perform thetechniques described herein. For example, a spectral profile extractor306 may be configured to extract a reference spectral profile from areference signal and a DUT spectral profile from a DUT signal. Aspectral profile comparator 308 may be configured to compare thereference spectral profile and the DUT spectral profile. A deltacalculator 310 may be configured to calculate a delta between thereference spectral profile and the DUT spectral profile. A correctionfilter designer 312 may be configured to design the correction filterusing the computed delta. In some embodiments, the modules 306-312 maybe modules of computer code configured to direct the operations of theprocessor 302.

The block diagram of FIG. 3 is not intended to indicate that the medium300 is to include all of the components shown in FIG. 3. Further, themedium 300 may include any number of additional components not shown inFIG. 3, depending on the details of the specific implementation.

FIG. 4A is an example according to the present techniques. A notebookcomputer running an exemplary ASR engine was used to evaluate theaccuracy of speech recognition. A set of high quality recordings wasidentified by their high ASR scores, i.e., low word error rates (WERs).The high quality recordings were used to compute the reference spectralprofile shown in FIG. 4A. The DUT spectral profile is shown in FIG. 4B.FIG. 4C shows the DUT spectral profile after application of thecorrection filter. In FIGS. 4A-4C, the x-axes 402A, 402B, and 402C arefrequency in Hertz and the y-axes 404A, 404B, and 404C are amplitude ona linear scale. FIG. 4D is the magnitude response of the correctionfilter calculated by techniques described herein. In FIG. 4D, the x-axisis frequency in Hertz and the y-axis is amplitude.

FIGS. 4E and 4F show the ASR accuracy improvements obtained when thecorrection filter was applied to speech signals. The speech signals weregenerated by broadcasting speech recordings over a main loudspeakerexternal to the DUT. The data shown in FIG. 4E was obtained in theabsence of an additional loudspeaker. In contrast, FIG. 4F shows theresults obtained in the presence of an additional side loudspeakersimulating a second user and masking the speech signals propagated bythe main loudspeaker. The speech signals were obtained with the mainloudspeaker positioned at different angles to the DUT. (At 00, the mainloudspeaker is directly in front of the DUT.) In tables 400E and 400F,the angles are given in columns 402E and 402F. Scores are given as WERs404E and 404F, thus lower values are better. Rows 406E and 406F give theWERs without the application of the correction filter. Rows 408E and408F show the WERs with application of the correction filter designed bytechniques described herein. Rows 410E and 410F give the delta valuesused to design the correction filter. Column 412E and 412F are the meanvalues for the rows 406E, 406F, 408E, 408F, 410E, and 410F.

Example 1 is an electronic device for improving speech recognition of adevice under test (DUT). The electronic device includes an extractingunit to extract a reference spectral profile from a reference signal anda DUT spectral profile from a DUT signal; a comparing unit to comparethe reference spectral profile and the DUT spectral profile; a deltacalculating unit to compute a delta between the reference spectralprofile and the DUT spectral profile to obtain a computed delta; and adesign unit to design a correction filter based on the computed delta.

Example 2 includes the electronic device of example 1, including orexcluding optional features. In this example, the electronic deviceincludes a first calculating unit to calculate the reference signal froma set of recordings.

Example 3 includes the electronic device of any one of examples 1 to 2,including or excluding optional features. In this example, the designunit designs the correction filter using a plurality of recordings, andwherein the plurality of recordings are obtained from one or moredevices.

Example 4 includes the electronic device of any one of examples 1 to 3,including or excluding optional features. In this example, theelectronic device includes an application unit to apply the correctionfilter to a microphone of the DUT. Optionally, the electronic deviceincludes an orientation sensor to determine an orientation of the DUTand employ an appropriate correction filter. Optionally, the electronicdevice includes a proximity sensor to determine a distance from a userto the DUT and employ the appropriate correction filter. Optionally, theelectronic device includes an angle sensor to determine an angle betweenthe user and the DUT and employ the appropriate correction filter.Optionally, the electronic device includes a lid position sensor todetermine a position of a lid of the DUT and employ the appropriatecorrection filter.

Example 5 includes the electronic device of any one of examples 1 to 4,including or excluding optional features. In this example, theelectronic device includes one or more microphones to receive the DUTsignal. Optionally, the electronic device includes a recording unit torecord the DUT signal.

Example 6 is a method of improving speech recognition of a device undertest (DUT). The method includes extracting a reference spectral profilefrom a reference signal; extracting a DUT spectral profile from a DUTsignal; comparing the reference spectral profile and DUT spectralprofile; calculating a delta between the reference spectral profile andthe DUT spectral profile; and designing a correction filter using thedelta.

Example 7 includes the method of example 6, including or excludingoptional features. In this example, the method includes using a set ofspeech recordings to calculate a reference signal. Optionally, themethod includes averaging of amplitude spectra of the reference signalto obtain the reference spectral profile.

Example 8 includes the method of any one of examples 6 to 7, includingor excluding optional features. In this example, the method includesrecording a speech utterance using the DUT to obtain a recorded speechutterance. Optionally, the method includes broadcasting the recordedspeech utterance using a speaker to obtain a broadcasted speechutterance; and receiving the broadcasted speech utterance using amicrophone internal to the DUT to obtain a DUT signal. Optionally,broadcasting the recorded speech utterance comprises using a speakerexternal to the DUT. Optionally, broadcasting the recorded speechutterance comprises using a speaker internal to the DUT. Optionally, themethod includes averaging of amplitude spectra of the DUT signal toobtain the DUT spectral profile.

Example 9 includes the method of any one of examples 6 to 8, includingor excluding optional features. In this example, the method includescalculating the correction filter using a frequency-sampling method orany other technique for designing an audio signal filter.

Example 10 includes the method of any one of examples 6 to 9, includingor excluding optional features. In this example, the method includesapplying the correction filter to a signal produced by the microphoneinternal to the DUT. Optionally, applying the correction filtercomprises determining at least one of an orientation of the DUT, adistance from a user to the DUT, an angle between the user and the DUT,and a position of a lid of the DUT.

Example 11 is a computer-readable medium, comprising instructions that,when executed by the processor, direct the processor to improve speechrecognition of a device under test (DUT). The computer-readable mediumincludes instructions that direct the processor to extract a referencespectral profile from a reference signal; extract a DUT spectral profilefrom a DUT signal; compare the reference spectral profile and the DUTspectral profile; calculate a delta between the reference spectralprofile and the DUT spectral profile; and design a correction filterusing the delta.

Example 12 includes the computer-readable medium of example 11,including or excluding optional features. In this example, thecomputer-readable medium includes instructions to direct the processorto calculate the reference signal from a set of speech recordings.Optionally, the computer-readable medium includes instructions to directthe processor to average amplitude spectra of the reference signal toobtain the reference spectral profile.

Example 13 includes the computer-readable medium of any one of examples11 to 12, including or excluding optional features. In this example, thecomputer-readable medium includes instructions to direct the processorto receive a speech utterance via a microphone to obtain the DUT signal.Optionally, the computer-readable medium includes instructions to directthe processor to average amplitude spectra of the DUT signal to obtainthe DUT spectral profile.

Example 14 includes the computer-readable medium of any one of examples11 to 13, including or excluding optional features. In this example, thecomputer-readable medium includes instructions to direct the processorto apply the correction filter to a signal produced by the microphone.Optionally, the computer-readable medium includes instructions todetermine at least one of an orientation of the DUT, a distance from auser to the DUT, an angle between the user and the DUT, and a positionof a lid of the DUT when applying the correction filter.

Example 15 includes the computer-readable medium of any one of examples11 to 14, including or excluding optional features. In this example, thecomputer-readable medium includes instructions to calculate thecorrection filter using a frequency-sampling method or any othertechnique for designing an audio signal filter.

Example 16 is an apparatus for improving speech recognition of a deviceunder test (DUT). The apparatus includes instructions that direct theprocessor to a means for extracting a spectral profile, wherein themeans for extracting a spectral profile extracts a reference spectralprofile from a reference signal and a DUT spectral profile from a DUTsignal; a means for comparing the reference spectral profile and the DUTspectral profile; a means for calculating a delta between the referencespectral profile and the DUT spectral profile; and a means for designinga correction filter using the delta.

Example 17 includes the apparatus of example 16, including or excludingoptional features. In this example, the apparatus includes a means forcalculating the reference signal from a set of speech recordings.

Example 18 includes the apparatus of any one of examples 16 to 17,including or excluding optional features. In this example, the means forextracting a spectral profile averages amplitude spectra of thereference signal to obtain the reference spectral profile.

Example 19 includes the apparatus of any one of examples 16 to 18,including or excluding optional features. In this example, the apparatusincludes a means for recording a speech utterance to obtain a recordedspeech utterance. Optionally, the apparatus includes a means forbroadcasting the recorded speech utterance to obtain a broadcastedspeech utterance; and a means for receiving the broadcasted speechutterance to obtain a DUT signal. Optionally, the means for broadcastingthe recorded speech utterance is a speaker external to the DUT.Optionally, the means for broadcasting the recorded speech utterance isa speaker internal to the DUT. Optionally, the means for receiving thebroadcasted speech utterance is a microphone internal to the DUT.

Example 20 includes the apparatus of any one of examples 16 to 19,including or excluding optional features. In this example, the means forextracting a spectral profile averages amplitude spectra of the DUTsignal to obtain the DUT spectral profile.

Example 21 includes the apparatus of any one of examples 16 to 20,including or excluding optional features. In this example, the apparatusincludes a means for calculating the correction filter, wherein themeans for calculating the correction filter is a frequency-samplingmethod or any other technique for designing an audio signal filter.

Example 22 includes the apparatus of any one of examples 16 to 21,including or excluding optional features. In this example, the apparatusincludes a means for applying the correction filter to a signal producedby the microphone internal to the DUT. Optionally, the apparatusincludes a means for applying the correction filter, wherein the meansfor applying the correction filter determines at least one of anorientation of the DUT, a distance from a user to the DUT, an anglebetween the user and the DUT, and a position of a lid of the DUT.

Example 23 is a notebook computer with improved speech recognition. Thenotebook computer with improved speech recognition includes instructionsthat direct the processor to an extracting unit to extract a referencespectral profile from a reference signal and a notebook computerspectral profile from a notebook computer signal; a comparing unit tocompare the reference spectral profile and the notebook computerspectral profile; a delta calculating unit to compute a delta betweenthe reference spectral profile and the notebook computer spectralprofile to obtain a computed delta; and a design unit to design acorrection filter based on the computed delta.

Example 24 includes the notebook computer with improved speechrecognition of example 23, including or excluding optional features. Inthis example, the notebook computer with improved speech recognitionincludes a first calculating unit to calculate the reference signal froma set of recordings.

Example 25 includes the notebook computer with improved speechrecognition of any one of examples 23 to 24, including or excludingoptional features. In this example, the design unit designs thecorrection filter using a plurality of recordings, and wherein theplurality of recordings are obtained from one or more devices.

Example 26 includes the notebook computer with improved speechrecognition of any one of examples 23 to 25, including or excludingoptional features. In this example, the notebook computer with improvedspeech recognition includes an application unit to apply the correctionfilter to a microphone of the notebook computer. Optionally, thenotebook computer with improved speech recognition includes anorientation sensor to determine an orientation of the notebook computerand employ an appropriate correction filter. Optionally, the notebookcomputer with improved speech recognition includes a proximity sensor todetermine a distance from a user to the notebook computer and employ theappropriate correction filter. Optionally, the notebook computer withimproved speech recognition includes an angle sensor to determine anangle between the user and the notebook computer and employ theappropriate correction filter. Optionally, the notebook computer withimproved speech recognition includes a lid position sensor to determinea position of a lid of the notebook computer and employ the appropriatecorrection filter.

Example 27 includes the notebook computer with improved speechrecognition of any one of examples 23 to 26, including or excludingoptional features. In this example, the notebook computer with improvedspeech recognition includes one or more microphones to receive thenotebook computer signal. Optionally, the notebook computer withimproved speech recognition includes a recording unit to record thenotebook computer signal.

Some embodiments may be implemented in one or a combination of hardware,firmware, and software. Some embodiments may also be implemented asinstructions stored on the tangible, non-transitory, machine-readablemedium, which may be read and executed by a computing platform toperform the operations described. In addition, a machine-readable mediummay include any mechanism for storing or transmitting information in aform readable by a machine, e.g., a computer. For example, amachine-readable medium may include read only memory (ROM); randomaccess memory (RAM); magnetic disk storage media; optical storage media;flash memory devices; or electrical, optical, acoustical or other formof propagated signals, e.g., carrier waves, infrared signals, digitalsignals, or the interfaces that transmit and/or receive signals, amongothers.

An embodiment is an implementation or example. Reference in thespecification to “an embodiment,” “one embodiment,” “some embodiments,”“various embodiments,” or “other embodiments” means that a particularfeature, structure, or characteristic described in connection with theembodiments is included in at least some embodiments, but notnecessarily all embodiments, of the present techniques. The variousappearances of “an embodiment,” “one embodiment,” or “some embodiments”are not necessarily all referring to the same embodiments.

Not all components, features, structures, characteristics, etc.described and illustrated herein need be included in a particularembodiment or embodiments. If the specification states a component,feature, structure, or characteristic “may”, “might”, “can” or “could”be included, for example, that particular component, feature, structure,or characteristic is not required to be included. If the specificationor claim refers to “a” or “an” element, that does not mean there is onlyone of the element. If the specification or claims refer to “anadditional” element, that does not preclude there being more than one ofthe additional element.

It is to be noted that, although some embodiments have been described inreference to particular implementations, other implementations arepossible according to some embodiments. Additionally, the arrangementand/or order of circuit elements or other features illustrated in thedrawings and/or described herein need not be arranged in the particularway illustrated and described. Many other arrangements are possibleaccording to some embodiments.

In each system shown in a figure, the elements in some cases may eachhave a same reference number or a different reference number to suggestthat the elements represented could be different and/or similar.However, an element may be flexible enough to have differentimplementations and work with some or all of the systems shown ordescribed herein. The various elements shown in the figures may be thesame or different. Which one is referred to as a first element and whichis called a second element is arbitrary.

It is to be understood that specifics in the aforementioned examples maybe used anywhere in one or more embodiments. For instance, all optionalfeatures of the computing device described above may also be implementedwith respect to either of the method or the computer-readable mediumdescribed herein. Furthermore, although flow diagrams and/or statediagrams may have been used herein to describe embodiments, thetechniques are not limited to those diagrams or to correspondingdescriptions herein. For example, flow need not move through eachillustrated box or state or in exactly the same order as illustrated anddescribed herein.

The present techniques are not restricted to the particular detailslisted herein. Indeed, those skilled in the art having the benefit ofthis disclosure will appreciate that many other variations from theforegoing description and drawings may be made within the scope of thepresent techniques. Accordingly, it is the following claims includingany amendments thereto that define the scope of the present techniques.

What is claimed is:
 1. An electronic device for improving speechrecognition of a device under test (DUT), comprising: an extracting unitto extract a reference spectral profile from a reference signal and aDUT spectral profile from a DUT signal; a comparing unit to compare thereference spectral profile and the DUT spectral profile; a deltacalculating unit to compute a delta between the reference spectralprofile and the DUT spectral profile to obtain a computed delta; adesign unit to design a correction filter based on the computed delta;and an application unit to apply the correction filter to a signalproduced by a microphone internal to the DUT to improve speechrecognition.
 2. The electronic device of claim 1, comprising a firstcalculating unit to calculate the reference signal from a set ofrecordings.
 3. The electronic device of claim 1, wherein the design unitdesigns the correction filter using a plurality of recordings, andwherein the plurality of recordings are obtained from one or moredevices.
 4. The electronic device of claim 1, comprising an orientationsensor to determine an orientation of the DUT and employ an appropriatecorrection filter.
 5. The electronic device of claim 1, comprising aproximity sensor to determine a distance from a user to the DUT andemploy the appropriate correction filter.
 6. The electronic device ofclaim 1, comprising an angle sensor to determine an angle between theuser and the DUT and employ the appropriate correction filter.
 7. Theelectronic device of claim 1, comprising a lid position sensor todetermine a position of a lid of the DUT and employ the appropriatecorrection filter.
 8. The electronic device of claim 1, comprising oneor more microphones to receive the DUT signal.
 9. The electronic deviceof claim 8, comprising a recording unit to record the DUT signal.
 10. Amethod, comprising: extracting a reference spectral profile from areference signal; extracting a device-under-test (DUT) spectral profilefrom a DUT signal; comparing the reference spectral profile and DUTspectral profile; calculating a delta between the reference spectralprofile and the DUT spectral profile; designing a correction filterusing the delta; and applying the correction filter to a signal producedby a microphone internal to the DUT to improve speech recognition. 11.The method of claim 10, comprising using a set of speech recordings tocalculate a reference signal.
 12. The method of claim 11, comprisingaveraging of amplitude spectra of the reference signal to obtain thereference spectral profile.
 13. The method of claim 10, comprisingrecording a speech utterance using the DUT to obtain a recorded speechutterance.
 14. The method of claim 13, comprising: broadcasting therecorded speech utterance using a speaker to obtain a broadcasted speechutterance; and receiving the broadcasted speech utterance using amicrophone internal to the DUT to obtain a DUT signal.
 15. The method ofclaim 14, comprising averaging of amplitude spectra of the DUT signal toobtain the DUT spectral profile.
 16. The method of claim 10, comprisingcalculating the correction filter using a frequency-sampling method orany other technique for designing an audio signal filter.
 17. The methodof claim 10, wherein applying the correction filter comprisesdetermining at least one of an orientation of the DUT, a distance from auser to the DUT, an angle between the user and the DUT, and a positionof a lid of the DUT.
 18. At least one non-transitory computer-readablemedium, comprising instructions to direct a processor to: extract areference spectral profile from a reference signal; extract adevice-under-test (DUT) spectral profile from a DUT signal; compare thereference spectral profile and the DUT spectral profile; calculate adelta between the reference spectral profile and the DUT spectralprofile; design a correction filter using the delta; and apply thecorrection filter to a signal produced by a microphone internal to theDUT to improve speech recognition.
 19. At least one non-transitorycomputer-readable medium of claim 18, comprising instructions to directthe processor to calculate the reference signal from a set of speechrecordings.
 20. At least one non-transitory computer-readable medium ofclaim 19, comprising instructions to direct the processor to averageamplitude spectra of the reference signal to obtain the referencespectral profile.
 21. At least one non-transitory computer-readablemedium of claim 18, comprising instructions to direct the processor toreceive a speech utterance via the microphone to obtain the DUT signal.22. At least one non-transitory computer-readable medium of claim 21,comprising instructions to direct the processor to average amplitudespectra of the DUT signal to obtain the DUT spectral profile.